AI Communications - Volume 25, issue 1

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ISSN 0921-7126 (P)
ISSN 1875-8452 (E)

Impact Factor 2019: 0.765

AI Communications is a journal on Artificial Intelligence (AI) which has a close relationship to ECCAI (the European Coordinating Committee for Artificial Intelligence). It covers the whole AI community: scientific institutions as well as commercial and industrial companies.

AI Communications aims to enhance contacts and information exchange between AI researchers and developers, and to provide supranational information to those concerned with AI and advanced information processing. AI Communications publishes refereed articles concerning scientific and technical AI procedures, provided they are of sufficient interest to a large readership of both scientific and practical background. In addition it contains high-level background material, both at the technical level as well as the level of opinions, policies and news. The Editorial and Advisory Board is appointed by the Editor-in-Chief.

Abstract: Before signing electronic contracts, a rational agent should estimate the expected utilities of these contracts and calculate the violation risks related to them. In order to perform such pre-signing procedures, this agent has to be capable of computing a policy taking into account the norms and sanctions in the contracts. In relation to this, the contribution of this work is threefold. First, we present the Normative Markov Decision Process, an extension of the Markov Decision Process for explicitly representing norms. In order to illustrate the usage of our framework, we model an example in a simulated aerospace aftermarket. Second, we…specify an algorithm for identifying the states of the process which characterize the violation of norms. Finally, we show how to compute policies with our framework and how to calculate the risk of violating the norms in the contracts by adopting a particular policy.
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Abstract: The state-of-the-art within Artificial Intelligence has directly benefited from research conducted within the computer poker domain. One such success has been the advancement of bottom up equilibrium finding algorithms via computational game theory. On the other hand, alternative top down approaches, that attempt to generalise decisions observed within a collection of data, have not received as much attention. In this work we employ a top down approach in order to construct case-based strategies within three computer poker domains. Our analysis begins within the simplest variation of Texas Hold'em poker, i.e. two-player, limit Hold'em. We trace the evolution of our case-based…architecture and evaluate the effect that modifications have on strategy performance. The end result of our experimentation is a coherent framework for producing strong case-based strategies based on the observation and generalisation of expert decisions. The lessons learned within this domain offer valuable insights, that we use to apply the framework to the more complicated domains of two-player, no-limit Hold'em and multi-player, limit Hold'em. For each domain we present results obtained from the Annual Computer Poker Competition, where the best poker agents in the world are challenged against each other. We also present results against human opposition.
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Abstract: The CADE ATP System Competition (CASC) is an annual evaluation of fully automatic, classical logic Automated Theorem Proving (ATP) systems. CASC-23 was the sixteenth competition in the CASC series. Thirty-six ATP systems and system variants competed in the various competition and demonstration divisions. An outline of the competition design, and a commentated summary of the results, are presented.

Abstract: We propose a novel methodology to extract gene expression profiles from microarray experiments based on the application of an AI process (multiobjective optimization) to establish relationships between each profile and the most suitable existing technique to discover it. We first determine a space of potential hypothesis (techniques) aggregating well-known and widely used methods. Then, for each profile, we perform a multiobjective search over the space of hypothesis in order to find the technique (or aggregation of them) that better finds it.

Abstract: Knowledge discovery techniques try to extract patterns and concepts from raw data, and clustering certainly is one of the most popular processes in this research field. However, nowadays data is being produced in streaming fashion and distributed locations, turning most classical methods obsolete. This thesis addresses two different clustering problems in ubiquitous and streaming scenarios, presenting evidence of the advantages produced by applying distributed and streaming machine learning algorithms, and proposing new ones to solve the addressed problems.